Chat Management With Iterative Refinement for Ambiguous Search Queries
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Solution Overview
Problem
Existing search systems struggle with handling ambiguous or poorly formulated search queries, failing to efficiently determine desired outcomes when initial inputs lack clarity or are unrelated to specific search criteria.
Innovation Solution
A computer system with a chat engine that initiates autonomous chats, records chat states, and uses a chatbot to select responses from a library based on search criteria, incorporating human and programmatic resources to progressively refine queries through iterative interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search system uses well-formulated queries, then search accuracy is improved, but the system cannot handle ambiguous or poorly formulated queries effectively
Solution Approach 1:
The chatbot implements iterative feedback loops where user responses are continuously analyzed and used to refine search criteria. The system asks clarifying questions based on ambiguous inputs, receives feedback, and progressively narrows down search parameters until a well-formulated query is achieved, resolving the contradiction between handling poor queries and maintaining search accuracy.
Solution Approach 2:
The system performs preliminary actions by proactively analyzing user intent even in ambiguous queries, pre-processing the input to identify potential search directions, and preparing clarifying questions in advance. This preliminary analysis enables the system to handle diverse query types while maintaining accurate search outcomes.
2Loss of information
If the system uses iterative chat interactions to refine queries, then query understanding is improved, but the time to resolve the search is increased
Solution Approach 1:
The chatbot applies partial action by selectively asking only the necessary clarifying questions based on the specific ambiguities detected in each query, rather than following a fixed multi-step interrogation process. This reduces the number of interactions needed while still achieving sufficient query understanding to perform accurate searches.
Solution Approach 2:
The system performs preliminary analysis of the ambiguous query to identify the most critical missing information, prioritizes asking the most impactful clarifying questions first, and uses heuristics to estimate when sufficient understanding has been achieved, thereby reducing overall resolution time while maintaining comprehension quality.
3Extent of automation
If the system autonomously manages chat state and selects responses, then automation level is improved, but the complexity of the system increases
Solution Approach 1:
The chat management system is segmented into distinct modular components: chat state manager, search criterion analyzer, response selector, and chatbot interface. Each module handles a specific aspect of the automation, making the overall complex system manageable through clear separation of concerns while maintaining high automation levels.
Solution Approach 2:
The chatbot implements universal response selection logic that can handle multiple types of queries (informational, navigational, transactional) and various states of ambiguity using the same core automation framework. This multi-functionality reduces the need for separate specialized systems, managing complexity while maintaining extensive automation capability.
Data Source
AI summary
A system receives a chat query based on a user interaction on a computing device of user. The system parses the chat query to identify one or more word patterns in the chat query, and determines a matching plug-in from the plug-in library based at least in part on the one or more word patterns in the chat query. The system utilizes the matching plug-in to access one or more resources and determine a chat response for the chat query, and transmit, over the one or more networks, the chat response to the computing device of the user.